[PDF] Top 20 Using Multi Sense Vector Embeddings for Reverse Dictionaries
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Using Multi Sense Vector Embeddings for Reverse Dictionaries
... same vector representa- tion, ...single vector representation is used, then this representation is likely to primarily reflect the word’s most prominent sense, while ne- glecting other meanings (see ... See full document
12
Syntax Aware Multi Sense Word Embeddings for Deep Compositional Models of Meaning
... As our compositional architectures we use a RecNN and an RNN. In the RecNN case, the words are composed by following the result of an external parser, while for the RNN the composi- tion takes place in sequence from left ... See full document
12
Real Multi Sense or Pseudo Multi Sense: An Approach to Improve Word Representation
... pseudo multi-sense, which is the word embedding models often embed one meaning to multiple senses, to describe the common problem in multi-sense word ...pseudo multi-sense from ... See full document
10
Beyond Bilingual: Multi sense Word Embeddings using Multilingual Context
... of using French and Spanish contexts to disambiguate the financial sense of the English word ...(financial) sense vector of bank will be used to predict vector of banco (Spanish ... See full document
10
Bilingual Learning of Multi sense Embeddings with Discrete Autoencoders
... with sense induction as a separate, clustering problem that is followed by an embedding learning compo- nent (Huang et ...the sense assignment and the em- beddings are trained jointly (Neelakantan et ... See full document
11
Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings
... makes sense to combine the two modalities into a multi- modal ...created using methods such as Canonical Corre- lation Analysis (CCA) and Deep Canonical Corre- lation Analysis (DCCA) (Hardoon et ... See full document
11
Implicit Subjective and Sentimental Usages in Multi sense Word Embeddings
... method using contextual difference for sense clus- tering to decide senses are so sensitive to contex- tual variation and usage of word, therefore may embed a single sense into several ... See full document
6
Do Multi Sense Embeddings Improve Natural Language Understanding?
... We first use the Greedy or Expectation strate- gies to obtain word vectors for tokens given their context. These vectors are then used as input to get the value of cosine similarity between two words. Performances are ... See full document
11
Multi Sense Embeddings from Topic Models
... samples, embeddings of dimension- ality 200, and fix the number of topics to ...topics, using perplexity score, can be found later in the analysis ...the embeddings using pre-trained GloVe ... See full document
8
Evaluating multi sense embeddings for semantic resolution monolingually and in word translation
... what less direct, and not always perceived by the speakers. This problem affects our sources as well: the Collins-COBUILD (CED, Sinclair (1987)) dictionary starts with the semantic distinc- tions and subordinates POS ... See full document
7
Probabilistic FastText for Multi Sense Word Embeddings
... One shortcoming with the above approaches to word embedding that are based on a prede- fined dictionary (termed as dictionary-based em- beddings) is their inability to learn representa- tions of rare words. To overcome ... See full document
11
Integrating WordNet for Multiple Sense Embeddings in Vector Semantics
... While vector representations of word meaning are capable of capturing important semantic fea- tures of words and performing tasks like meaning comparison and analogizing, one of their short- comings is their ... See full document
8
Multi sense Embeddings through a Word Sense Disambiguation Process
... and vector sizes of 300 and 1000 ...implemented using Python 3.6.5, with NLTK (Natural Language Toolkit) 3.2.5, using the gensim ...5.5, using two different training datasets (WD10 and WD18) ... See full document
15
Making Sense of Word Embeddings
... dense vector spaces with neural ...word embeddings and ...Finally, embeddings are re-trained on this sense-labeled ...learns multi- ple sense-aware prototypes weighted by their ... See full document
10
Joint Learning of Sense and Word Embeddings
... single sense for each word (fourth, fifth, sixth and seventh column) using both labelled and unlabelled corpora, unlike the mixture of the various senses (third column) produced by using only un- ... See full document
7
Improving Word Sense Disambiguation in Neural Machine Translation with Sense Embeddings
... Word sense disambiguation is necessary in translation because different word senses often have different ...word sense disambiguation has so far not been ...word sense disam- biguation task that is ... See full document
9
Unsupervised Most Frequent Sense Detection using Word Embeddings
... Word Sense Disam- biguation (WSD) algorithm is its performance against the Most Frequent Sense ...requires sense annotated corpus in enormous amounts, which is out of bounds for most languages, even ... See full document
6
All words Word Sense Disambiguation Using Concept Embeddings
... method using only word embeddings of syn- onyms, 2) a method using both word and concept embed- dings of synonyms, and 3) a method using only concept embeddings of ... See full document
6
Sense Embeddings in Knowledge Based Word Sense Disambiguation
... of sense embeddings creation and Lesk extension can be easily adapted to many language, requiring only a set of unannotated corpora, and a typical dictionary, thus, giving the possibility to create an ... See full document
7
MUSE: Modularizing Unsupervised Sense Embeddings
... word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given ...inefficient sense selec- ...purely ... See full document
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